This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The concepts and techniques in this course will serve as building blocks for the inference and modeling courses in the Specialization.
Ce cours fait partie de la Spécialisation Statistics with R
Introduction to Probability and Data
proposé par
À propos de ce cours
4.7
2,824 notes
Compétences que vous acquerrez
StatisticsR ProgrammingRstudioExploratory Data Analysis
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
112 minutes pour terminer
About Introduction to Probability and Data
<p>This course introduces you to sampling and exploring data, as well as basic probability theory. You will examine various types of sampling methods and discuss how such methods can impact the utility of a data analysis. The concepts in this module will serve as building blocks for our later courses.<p>Each lesson comes with a set of learning objectives that will be covered in a series of short videos. Supplementary readings and practice problems will also be suggested from <a href="https://leanpub.com/openintro-statistics/" target="_blank">OpenIntro Statistics, 3rd Edition</a> (a free online introductory statistics textbook, that I co-authored). There will be weekly quizzes designed to assess your learning and mastery of the material covered that week in the videos. In addition, each week will also feature a lab assignment, in which you will use R to apply what you are learning to real data. There will also be a data analysis project designed to enable you to answer research questions of your own choosing.<p>Since this is a Coursera course, you are welcome to participate as much or as little as you’d like, though I hope that you will begin by participating fully. One of the most rewarding aspects of a Coursera course is participation in forum discussions about the course materials. Please take advantage of other students' feedback and insight and contribute your own perspective where you see fit to do so. You can also check out the <a href="https://www.coursera.org/learn/probability-intro/resources/crMc4" target="_blank">resource page</a> listing useful resources for this course. <p>Thank you for joining the Introduction to Probability and Data community! Say hello in the Discussion Forums. We are looking forward to your participation in the course.</p>
1 vidéo (Total 2 min), 1 lecture
1 vidéo
1 lecture
More about Introduction to Probability and Data10 min
1 heure pour terminer
Introduction to Data
<p>Welcome to Introduction to Probability and Data! I hope you are just as excited about this course as I am! In the next five weeks, we will learn about designing studies, explore data via numerical summaries and visualizations, and learn about rules of probability and commonly used probability distributions. If you have any questions, feel free to post them on <a href="https://www.coursera.org/learn/probability-intro/module/rQ9Al/discussions?sort=lastActivityAtDesc&page=1" target="_blank"><b>this module's forum</b></a> and discuss with your peers! To get started, view the <a href="https://www.coursera.org/learn/probability-intro/supplement/rooeY/lesson-learning-objectives" target="_blank"><b>learning objectives</b></a> of Lesson 1 in this module.</p>
6 vidéos (Total 28 min), 2 lectures, 2 quiz
6 vidéos
Introduction3 min
Data Basics5 min
Observational Studies & Experiments4 min
Sampling and sources of bias8 min
Experimental Design2 min
(Spotlight) Random Sample Assignment3 min
2 lectures
Lesson Learning Objectives10 min
Suggested Readings and Practice10 min
2 exercices pour s'entraîner
Week 1 Practice Quiz10 min
Week 1 Quiz14 min
2 heures pour terminer
Introduction to Data: Lesson Choices
Choice 1: To complete this assignment you will use R and RStudio installed on your local computer. | Choice 2: To complete this assignment you will use an online version of R and RStudio hosted by Datacamp.
1 vidéo (Total 2 min), 3 lectures, 2 quiz
1 vidéo
3 lectures
About Lesson Choices (Read Before Selection)10 min
Week 1 Lab Instructions (RStudio)10 min
Week 1 Lab Instructions (DataCamp)10 min
1 exercice pour s'entraîner
Week 1 Lab: Introduction to R and RStudio16 min
Semaine
22 heures pour terminer
Exploratory Data Analysis and Introduction to Inference
<p>Welcome to Week 2 of Introduction to Probability and Data! Hope you enjoyed materials from Week 1. This week we will delve into numerical and categorical data in more depth, and introduce inference. </p>
7 vidéos (Total 46 min), 3 lectures, 2 quiz
7 vidéos
Measures of Center4 min
Measures of Spread6 min
Robust Statistics1 min
Transforming Data3 min
Exploring Categorical Variables8 min
Introduction to Inference12 min
3 lectures
Lesson Learning Objectives10 min
Lesson Learning Objectives10 min
Suggested Readings and Practice10 min
2 exercices pour s'entraîner
Week 2 Practice Quiz10 min
Week 2 Quiz12 min
2 heures pour terminer
Exploratory Data Analysis and Introduction to Inference: Lesson Choices
Choice 1: To complete this assignment you will use R and RStudio installed on your local computer. | Choice 2: To complete this assignment you will use an online version of R and RStudio hosted by Datacamp.
2 lectures, 2 quiz
2 lectures
Week 2 Lab Instructions (RStudio)10 min
Week 2 Lab Instructions (DataCamp)10 min
1 exercice pour s'entraîner
Week 2 Lab: Introduction to Data20 min
Semaine
32 heures pour terminer
Introduction to Probability
<p>Welcome to Week 3 of Introduction to Probability and Data! Last week we explored numerical and categorical data. This week we will discuss probability, conditional probability, the Bayes’ theorem, and provide a light introduction to Bayesian inference. </p><p>Thank you for your enthusiasm and participation, and have a great week! I’m looking forward to working with you on the rest of this course. </p>
9 vidéos (Total 82 min), 3 lectures, 2 quiz
9 vidéos
Introduction5 min
Disjoint Events + General Addition Rule9 min
Independence9 min
Probability Examples9 min
(Spotlight) Disjoint vs. Independent2 min
Conditional Probability12 min
Probability Trees10 min
Bayesian Inference14 min
Examples of Bayesian Inference7 min
3 lectures
Lesson Learning Objectives10 min
Lesson Learning Objectives10 min
Suggested Readings and Practice10 min
2 exercices pour s'entraîner
Week 3 Practice Quiz6 min
Week 3 Quiz10 min
2 heures pour terminer
Introduction to Probability: Lesson Choices
Choice 1: To complete this assignment you will use R and RStudio installed on your local computer. | Choice 2: To complete this assignment you will use an online version of R and RStudio hosted by Datacamp.
2 lectures, 2 quiz
2 lectures
Week 3 Lab Instructions (RStudio)10 min
Week 3 Lab Instructions (DataCamp)10 min
1 exercice pour s'entraîner
Week 3 Lab: Probability10 min
Semaine
42 heures pour terminer
Probability Distributions
<p>Great work so far! Welcome to Week 4 -- the last content week of Introduction to Probability and Data! This week we will introduce two probability distributions: the normal and the binomial distributions in particular. As usual, you can evaluate your knowledge in this week's quiz. There will be <b>no labs</b> for this week. Please don't hesitate to post any questions, discussions and related topics on <a href="https://www.coursera.org/learn/probability-intro/module/VdVNg/discussions?sort=lastActivityAtDesc&page=1" target="_blank"><b>this week's forum</b></a>.</p>
6 vidéos (Total 67 min), 4 lectures, 2 quiz
6 vidéos
Normal Distribution17 min
Evaluating the Normal Distribution2 min
Working with the Normal Distribution5 min
Binomial Distribution17 min
Normal Approximation to Binomial14 min
Working with the Binomial Distribution9 min
4 lectures
Lesson Learning Objectives10 min
Lesson Learning Objectives10 min
Suggested Readings and Practice10 min
Data Analysis Project Example10 min
2 exercices pour s'entraîner
Week 4 Practice Quiz14 min
Week 4 Quiz14 min
4.7
635 avis35%
a commencé une nouvelle carrière après avoir terminé ces cours
29%
a bénéficié d'un avantage concret dans sa carrière grâce à ce cours
Meilleurs avis
This course literally taught me a lot, the concepts were beautifully explained but the way it was delivered and overall exercises and the difficulty of problems made it more challenging and enjoying.
The tutor makes it really simple. The given examples really helped to understand the concepts and apply it to a wide range of problems. Thank you for this. Wish I could complete the assignments too.
Enseignant
Mine Çetinkaya-Rundel
Associate Professor of the PracticeDepartment of Statistical Science
À propos de Université Duke
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
À propos de la Spécialisation Statistics with R
In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis.
You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions.
Foire Aux Questions
Quand aurai-je accès aux vidéos de cours et aux devoirs ?
Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.
À quoi ai-je droit si je m'abonne à cette Spécialisation ?
Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.
Quelle est la politique de remboursement ?
Une aide financière est-elle possible ?
Will I receive a transcript from Duke University for completing this course?
No. Completion of a Coursera course does not earn you academic credit from Duke; therefore, Duke is not able to provide you with a university transcript. However, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
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